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Learning to rank in structured prediction models

Thomas Mensink, Jakob Verbeek, Tiberio Caetano
Ranking-based performance measures, such as precision-at-k and averageprecision, are frequently used in information retrieval and image annotation. Often the loss used to train models for such tasks is different from the loss that is used to measure performance at test time. Only recently methods have appeared that explicitly train to minimize such ranking losses. Motivated by image annotation tasks where there are strong dependencies among the labels that should be ranked, we are interested in accommodating for pairwise interactions in ranking models: a feature not present in existing methods that optimize for ranking losses. We con sider a generic family of such ranking models, where inference takes the form of a quadratic assignment problem which is generally NP-hard. We identify a subclass of such models with star-structured interactions, where inference can be in poly nomial time. In particular, for the precision-at-k loss, this leads to a model where inference is linear in the number of elements to rank, and exponential in the size of the number of elements in the fully connected clique at the center of the star. We validate the model on a public benchmark image annotation data set.
NIPS, Sierra Nevada, Spain, December 16-17, 2011.